The first of these modules upsamples the feature map by a factor of 4 using a stride-two \(2\times 2\)  transposed convolution, followed by group normalization [1]} and GeLU [2]}, and finally another stride-two \(2\times 2\)  transposed convolution. The next \(d/4\) th block's output is upsampled by \(2\times \)  using a single stride-two \(2\times 2\)  transposed convolution (without normalization and non-linearity). The next \(d/4\) th block's output is taken as is and the final ViT block's output is downsampled by a factor of two using stride-two \(2\times 2\)  max pooling. Each of these modules preserves the ViT's embedding/channel dimension. Assuming a patch size of 16, these modules produce feature maps with strides of 4, 8, 16, and 32 pixels, the input image, that are ready to input into an FPN.
